Global health surveillance is currently facing a challenge of Knowledge Gaps. While general-purpose AI has proliferated, it remains fundamentally unsuited for the high-stakes epidemiological domain due to chronic hallucinations and an inability to navigate specialized data silos. This paper introduces ARIES (Agentic Retrieval Intelligence for Epidemiological Surveillance), a specialized, autonomous multi-agent framework designed to move beyond static, disease-specific dashboards toward a dynamic intelligence ecosystem. Built on a hierarchical command structure, ARIES utilizes GPTs to orchestrate a scalable swarm of sub-agents capable of autonomously querying World Health Organization (WHO), Center for Disease Control and Prevention (CDC), and peer-reviewed research papers. By automating the extraction and logical synthesis of surveillance data, ARIES provides a specialized reasoning that identifies emergent threats and signal divergence in near real-time. This modular architecture proves that a task-specific agentic swarm can outperform generic models, offering a robust, extensible for next-generation outbreak response and global health intelligence.

翻译：全球健康监测当前正面临知识鸿沟的挑战。尽管通用人工智能已广泛普及，但由于其长期存在的幻觉问题及无法有效处理专业数据孤岛，该技术从根本上仍不适用于高风险的流行病学领域。本文介绍了ARIES（用于流行病学监测的智能体检索智能），这是一个专业的自主多智能体框架，旨在超越静态的、针对特定疾病的仪表盘，转向动态的智能生态系统。ARIES基于分层指令结构构建，利用GPT来编排一个可扩展的子智能体群，这些子智能体能够自主查询世界卫生组织（WHO）、疾病控制与预防中心（CDC）以及同行评议的研究论文。通过自动化提取与逻辑综合监测数据，ARIES提供了专业的推理能力，能够近乎实时地识别新发威胁与信号偏差。这种模块化架构证明，针对特定任务的智能体群能够超越通用模型，为下一代疫情应对与全球健康智能提供了一个稳健、可扩展的解决方案。